Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
This pr updates the ASE calculator interface to accommodate a few changes:
Use models that were not trained with matsciml. This eliminates the requirement for the model to be one of ForceRegressionTask, etc.
Map from model output keys to keys that are expected by ASE. Currently, the calculator manually renames model outputs to expected outputs by ase, for example the model output force gets mapped into forces. This mapping should now be passes as a dictionary to the calculator on instantiation if required.
The calculator will now iterate through the properties passed into _calculate to build the results dictionary.
Add the concatenate_keys function which also adds properties to PyG graphs that are expected by models like MACE, add a few extra keys to _format_atoms, and change when type casting is called.
Updated tests to reflect new changes, added test using a pretrained matgl model.
This pr updates the ASE calculator interface to accommodate a few changes:
ForceRegressionTask, etc.forcegets mapped intoforces. This mapping should now be passes as a dictionary to the calculator on instantiation if required._calculateto build the results dictionary.concatenate_keysfunction which also adds properties to PyG graphs that are expected by models like MACE, add a few extra keys to_format_atoms, and change when type casting is called.matglmodel.